“Uncanny Valley 2” is a study that examines children’s beliefs and feelings about a collection of real-world robots based on a viewing of an 8-second video of that robot.

Female Male
54 66
## [1] "2017-02-18"
## [1] "2018-02-08"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     0.0     9.0    14.0    18.1    22.5    54.0
## [1] 120
  Female Male Sum
3-6 15 21 36
6-9 18 22 40
9-12 17 17 34
>12 4 6 10
Sum 54 66 120
Min. 1st Qu. Median Mean 3rd Qu. Max.
3.413 5.408 7.772 7.914 9.789 17.46
## [1] 120
## [1] 117
## [1] 113

Descriptives

Analysis of Questions

Confirmatory Factor Analysis

## lavaan (0.5-23.1097) converged normally after  82 iterations
## 
##   Number of observations                           288
## 
##   Estimator                                         ML
##   Minimum Function Test Statistic               45.797
##   Degrees of freedom                                17
##   P-value (Chi-square)                           0.000
## 
## Model test baseline model:
## 
##   Minimum Function Test Statistic              631.689
##   Degrees of freedom                                28
##   P-value                                        0.000
## 
## User model versus baseline model:
## 
##   Comparative Fit Index (CFI)                    0.952
##   Tucker-Lewis Index (TLI)                       0.921
## 
## Loglikelihood and Information Criteria:
## 
##   Loglikelihood user model (H0)              -3279.481
##   Loglikelihood unrestricted model (H1)      -3256.583
## 
##   Number of free parameters                         19
##   Akaike (AIC)                                6596.962
##   Bayesian (BIC)                              6666.558
##   Sample-size adjusted Bayesian (BIC)         6606.307
## 
## Root Mean Square Error of Approximation:
## 
##   RMSEA                                          0.077
##   90 Percent Confidence Interval          0.050  0.104
##   P-value RMSEA <= 0.05                          0.048
## 
## Standardized Root Mean Square Residual:
## 
##   SRMR                                           0.053
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Standard Errors                             Standard
## 
## Latent Variables:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   agency =~                                           
##     choose            1.000                           
##     think             1.032    0.107    9.663    0.000
##     moral             0.787    0.096    8.182    0.000
##   exp =~                                              
##     scared            1.000                           
##     pain              1.043    0.131    7.969    0.000
##     hungry            1.580    0.185    8.523    0.000
##   uv =~                                               
##     creepy            1.000                           
##     weird             2.995    2.986    1.003    0.316
## 
## Covariances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##   agency ~~                                           
##     exp               0.381    0.064    5.911    0.000
##     uv               -0.061    0.071   -0.860    0.390
##   exp ~~                                              
##     uv               -0.018    0.024   -0.736    0.462
## 
## Variances:
##                    Estimate  Std.Err  z-value  P(>|z|)
##    .choose            0.751    0.093    8.109    0.000
##    .think             0.629    0.088    7.120    0.000
##    .moral             0.997    0.097   10.266    0.000
##    .scared            0.743    0.072   10.348    0.000
##    .pain              0.608    0.063    9.692    0.000
##    .hungry            0.374    0.083    4.490    0.000
##    .creepy            1.120    0.256    4.378    0.000
##    .weird            -1.021    2.138   -0.478    0.633
##     agency            0.808    0.133    6.085    0.000
##     exp               0.380    0.079    4.807    0.000
##     uv                0.242    0.247    0.979    0.328
## $lambda
##        agency   exp    uv
## choose  0.720 0.000 0.000
## think   0.760 0.000 0.000
## moral   0.578 0.000 0.000
## scared  0.000 0.582 0.000
## pain    0.000 0.636 0.000
## hungry  0.000 0.847 0.000
## creepy  0.000 0.000 0.421
## weird   0.000 0.000 1.375
## 
## $theta
##        choose think  moral  scared pain   hungry creepy weird 
## choose  0.482                                                 
## think   0.000  0.422                                          
## moral   0.000  0.000  0.666                                   
## scared  0.000  0.000  0.000  0.662                            
## pain    0.000  0.000  0.000  0.000  0.595                     
## hungry  0.000  0.000  0.000  0.000  0.000  0.283              
## creepy  0.000  0.000  0.000  0.000  0.000  0.000  0.823       
## weird   0.000  0.000  0.000  0.000  0.000  0.000  0.000 -0.891
## 
## $psi
##        agency exp    uv    
## agency  1.000              
## exp     0.688  1.000       
## uv     -0.139 -0.059  1.000

Partial Correlations

A Priori Variables

## 
##  Pearson's product-moment correlation
## 
## data:  RBI$exp.c and RBI$agency.c
## t = 8.8, df = 290, p-value <0.0000000000000002
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3642 0.5469
## sample estimates:
##    cor 
## 0.4604
## 
## Call:
## lm(formula = uv.c ~ exp.c + agency.c + Sex + Age, data = RBI)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.253 -0.829 -0.371  0.631  2.438 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.01318    0.27410   -0.05    0.962  
## exp.c        0.12304    0.07029    1.75    0.081 .
## agency.c    -0.16103    0.06819   -2.36    0.019 *
## Sex          0.05185    0.11803    0.44    0.661  
## Age         -0.00832    0.02346   -0.35    0.723  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.995 on 283 degrees of freedom
## Multiple R-squared:  0.0239, Adjusted R-squared:  0.0101 
## F-statistic: 1.73 on 4 and 283 DF,  p-value: 0.143
## 
## Call:
## lm(formula = uv.c ~ exp.c + agency.c + Sex + Age, data = RBI)
## 
## Standardized Coefficients::
## (Intercept)       exp.c    agency.c         Sex         Age 
##     0.00000     0.12304    -0.16103     0.02588    -0.02412

K-means clustering of a priori variables

## [1] 95.2
HAHE HALE LAHE LALE
33 67 27 161
## 
## Call:
## lm(formula = uv ~ cluster.name, data = km)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.000 -0.894 -0.394  0.606  2.328 
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)        1.9394     0.1733   11.19 <0.0000000000000002 ***
## cluster.nameHALE  -0.2678     0.2117   -1.26                0.21    
## cluster.nameLAHE   0.0606     0.2583    0.23                0.81    
## cluster.nameLALE  -0.0450     0.1902   -0.24                0.81    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.995 on 284 degrees of freedom
## Multiple R-squared:  0.0116, Adjusted R-squared:  0.00116 
## F-statistic: 1.11 on 3 and 284 DF,  p-value: 0.345
## 
## Call:
## lm(formula = uv ~ cluster.name, data = km)
## 
## Standardized Coefficients::
##      (Intercept) cluster.nameHALE cluster.nameLAHE cluster.nameLALE 
##          0.00000         -0.11379          0.01777         -0.02247

Distribution of robots among k-means clusters

## 
## Call:
## lm(formula = uv ~ agency.c + exp.c + robot.group + Sex + Age, 
##     data = RBI)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.342 -0.772 -0.342  0.722  2.413 
## 
## Coefficients:
##                         Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)            1.6089901  0.3397258    4.74 0.0000035 ***
## agency.c              -0.1494664  0.0671771   -2.22     0.027 *  
## exp.c                  0.1194660  0.0692261    1.73     0.085 .  
## robot.grouphuman-like  0.4574003  0.2484684    1.84     0.067 .  
## robot.grouprobotic     0.0519473  0.2325755    0.22     0.823    
## Sex                    0.0645061  0.1161184    0.56     0.579    
## Age                   -0.0000625  0.0233074    0.00     0.998    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.978 on 281 degrees of freedom
## Multiple R-squared:  0.0554, Adjusted R-squared:  0.0353 
## F-statistic: 2.75 on 6 and 281 DF,  p-value: 0.013
## 
## Call:
## lm(formula = uv ~ agency.c + exp.c + robot.group + Sex + Age, 
##     data = RBI)
## 
## Standardized Coefficients::
##           (Intercept)              agency.c                 exp.c 
##             0.0000000            -0.1500786             0.1199553 
## robot.grouphuman-like    robot.grouprobotic                   Sex 
##             0.1992176             0.0243625             0.0323287 
##                   Age 
##            -0.0001818

Distribution of participants among k-means clusters

Number of participants that fall into 1, 2, 3, or 4 clusters
1 2 3
64 41 3
Number of participants that fall into only one cluster
0 HAHE HALE LAHE LALE
44 5 12 2 45
Number of participants that fall into only two clusters (continued below)
0 HAHE & HALE HAHE & LALE HALE & LALE LAHE & HAHE LAHE & HALE
67 7 2 18 2 3
LAHE & LALE
9

Data-driven aggregates

Principal Components Analysis

##           PC1     PC2     PC3     PC4     PC5     PC6      PC7
## choose 0.1427 0.14308 0.17844 0.05141 0.27243 0.16175 0.008895
## feel   0.1633 0.06358 0.10784 0.17450 0.04959 0.11595 0.344097
## hungry 0.1627 0.08363 0.07536 0.13784 0.04779 0.22678 0.290400
## moral  0.1347 0.15688 0.24831 0.03094 0.09743 0.15049 0.206442
## pain   0.1234 0.23797 0.08918 0.13082 0.13706 0.23174 0.055208
## scared 0.1246 0.20412 0.15516 0.27507 0.12476 0.02968 0.031383
## think  0.1486 0.11073 0.14571 0.19941 0.27093 0.08361 0.063575
## Importance of components:
##                          PC1   PC2    PC3    PC4    PC5    PC6    PC7
## Standard deviation     1.856 1.061 0.8364 0.7117 0.6728 0.6416 0.5979
## Proportion of Variance 0.492 0.161 0.0999 0.0724 0.0647 0.0588 0.0511
## Cumulative Proportion  0.492 0.653 0.7531 0.8255 0.8901 0.9489 1.0000

## 
##  Pearson's product-moment correlation
## 
## data:  pca$PC1 and pca$PC2
## t = -0.000000000000024, df = 290, p-value = 1
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1156  0.1156
## sample estimates:
##                   cor 
## -0.000000000000001424
## 
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group + Sex + Age, data = pca)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.440 -0.744 -0.322  0.678  2.572 
## 
## Coefficients:
##                       Estimate Std. Error t value  Pr(>|t|)    
## (Intercept)            1.66905    0.33593    4.97 0.0000012 ***
## PC1                    0.04181    0.03609    1.16    0.2477    
## PC2                    0.15015    0.05431    2.76    0.0061 ** 
## robot.grouphuman-like  0.45689    0.24667    1.85    0.0650 .  
## robot.grouprobotic     0.05655    0.23086    0.24    0.8067    
## Sex                    0.07034    0.11537    0.61    0.5426    
## Age                   -0.00905    0.02336   -0.39    0.6989    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.973 on 281 degrees of freedom
## Multiple R-squared:  0.0658, Adjusted R-squared:  0.0459 
## F-statistic:  3.3 on 6 and 281 DF,  p-value: 0.00374
## 
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group + Sex + Age, data = pca)
## 
## Standardized Coefficients::
##           (Intercept)                   PC1                   PC2 
##               0.00000               0.07793               0.15997 
## robot.grouphuman-like    robot.grouprobotic                   Sex 
##               0.19899               0.02652               0.03525 
##                   Age 
##              -0.02633

K-means clustering of a data-driven components

## [1] 250.7

Distribution of robots among k-means clusters (PCA)

##          
##           1 2 3
##   kf      1 0 0
##   pepper  1 0 0
##   sofia   1 0 0
##   atlas   0 1 0
##   nao     0 1 0
##   spot    0 1 0
##   stan    0 1 0
##   tapia   0 1 0
##   actroid 0 0 1
##   festo   0 0 1
##   kb      0 0 1
## 
## Call:
## lm(formula = uv ~ cluster, data = km)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.111 -0.750 -0.419  0.623  2.323 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   1.6769     0.1231   13.62 <0.0000000000000002 ***
## cluster2      0.2419     0.1468    1.65               0.100    
## cluster3      0.4342     0.2272    1.91               0.057 .  
## cluster4      0.0731     0.1965    0.37               0.710    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.992 on 284 degrees of freedom
## Multiple R-squared:  0.0173, Adjusted R-squared:  0.00691 
## F-statistic: 1.67 on 3 and 284 DF,  p-value: 0.175
## 
## Call:
## lm(formula = uv ~ cluster, data = km)
## 
## Standardized Coefficients::
## (Intercept)    cluster2    cluster3    cluster4 
##     0.00000     0.12137     0.12730     0.02594

Distribution of participants among k-means clusters (PCA)

Number of participants that fall into 1, 2, or 3 clusters
0 1 2 3
10 64 32 2
Number of participants that fall into only one cluster
0 1 2 3
53 8 43 4
Number of participants that fall into only two clusters
0 1 & 2 1 & 3 2 & 3
76 25 5 2

Imputed Data

Analysis of Questions

Exploratory/Confirmatory Factor Analysis

## ** WARNING ** lavaan (0.5-23.1097) did NOT converge after 747 iterations
## ** WARNING ** Estimates below are most likely unreliable
## 
##   Number of observations                          1243
## 
##   Estimator                                         ML
##   Minimum Function Test Statistic                   NA
##   Degrees of freedom                                NA
##   P-value                                           NA
## 
## Parameter Estimates:
## 
##   Information                                 Expected
##   Standard Errors                             Standard
## 
## Latent Variables:
##                    Estimate    Std.Err  z-value  P(>|z|)
##   agency =~                                             
##     choose              1.000                           
##     think               0.966       NA                  
##     moral               0.933       NA                  
##   exp =~                                                
##     scared              1.000                           
##     pain                0.943       NA                  
##     hungry              1.104       NA                  
##   uv =~                                                 
##     creepy              1.000                           
##     weird               0.000       NA                  
## 
## Covariances:
##                    Estimate    Std.Err  z-value  P(>|z|)
##   agency ~~                                             
##     exp                 0.705       NA                  
##     uv                  0.220       NA                  
##   exp ~~                                                
##     uv                  0.233       NA                  
## 
## Variances:
##                    Estimate    Std.Err  z-value  P(>|z|)
##    .choose              0.505       NA                  
##    .think               0.432       NA                  
##    .moral               0.515       NA                  
##    .scared              0.317       NA                  
##    .pain                0.326       NA                  
##    .hungry              0.251       NA                  
##    .creepy         -13276.450       NA                  
##    .weird               1.153       NA                  
##     agency              0.922       NA                  
##     exp                 0.712       NA                  
##     uv              13277.698       NA
## $lambda
##        agency   exp      uv
## choose  0.804 0.000   0.000
## think   0.816 0.000   0.000
## moral   0.780 0.000   0.000
## scared  0.000 0.832   0.000
## pain    0.000 0.813   0.000
## hungry  0.000 0.881   0.000
## creepy  0.000 0.000 103.156
## weird   0.000 0.000   0.005
## 
## $theta
##        choose     think      moral      scared     pain       hungry    
## choose      0.354                                                       
## think       0.000      0.334                                            
## moral       0.000      0.000      0.391                                 
## scared      0.000      0.000      0.000      0.308                      
## pain        0.000      0.000      0.000      0.000      0.339           
## hungry      0.000      0.000      0.000      0.000      0.000      0.224
## creepy      0.000      0.000      0.000      0.000      0.000      0.000
## weird       0.000      0.000      0.000      0.000      0.000      0.000
##        creepy     weird     
## choose                      
## think                       
## moral                       
## scared                      
## pain                        
## hungry                      
## creepy -10640.127           
## weird       0.000      1.000
## 
## $psi
##        agency exp   uv   
## agency 1.000             
## exp    0.870  1.000      
## uv     0.002  0.002 1.000

Partial Correlations

A Priori Variables

## 
##  Pearson's product-moment correlation
## 
## data:  RBI.imp$exp.c and RBI.imp$agency.c
## t = 39, df = 1200, p-value <0.0000000000000002
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7136 0.7640
## sample estimates:
##    cor 
## 0.7398
## 
## Call:
## lm(formula = uv.c ~ exp.c + agency.c + Sex + Age, data = RBI.imp)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.426 -0.805 -0.357  0.647  2.805 
## 
## Coefficients:
##             Estimate Std. Error t value    Pr(>|t|)    
## (Intercept)  -0.0603     0.0949   -0.64        0.53    
## exp.c         0.2333     0.0424    5.51 0.000000045 ***
## agency.c     -0.1689     0.0423   -4.00 0.000067806 ***
## SexMale      -0.0652     0.0565   -1.16        0.25    
## Age           0.0120     0.0105    1.14        0.25    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.989 on 1238 degrees of freedom
## Multiple R-squared:  0.0256, Adjusted R-squared:  0.0225 
## F-statistic: 8.14 on 4 and 1238 DF,  p-value: 0.00000177

K-means clustering of a priori variables

## [1] 370.9

Distribution of robots among k-means clusters

##          
##           1 2 3
##   festo   1 0 0
##   atlas   0 1 0
##   kb      0 1 0
##   kf      0 1 0
##   spot    0 1 0
##   actroid 0 0 1
##   nao     0 0 1
##   pepper  0 0 1
##   sofia   0 0 1
##   stan    0 0 1
##   tapia   0 0 1
## 
## Call:
## lm(formula = uv ~ cluster, data = km)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -0.943 -0.793 -0.293  0.707  2.467 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   1.7927     0.0315   57.00 <0.0000000000000002 ***
## cluster2      0.1502     0.0785    1.91              0.0558 .  
## cluster3     -0.2602     0.0828   -3.14              0.0017 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.951 on 1240 degrees of freedom
## Multiple R-squared:  0.0125, Adjusted R-squared:  0.0109 
## F-statistic: 7.86 on 2 and 1240 DF,  p-value: 0.000404
## 
## Call:
## lm(formula = uv ~ cluster, data = km)
## 
## Standardized Coefficients::
## (Intercept)    cluster2    cluster3 
##     0.00000     0.05465    -0.08969
## 
## Call:
## lm(formula = uv ~ agency.c + exp.c + robot.group + Sex + Age, 
##     data = RBI)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.481 -0.751 -0.359  0.615  2.627 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             1.3467     0.1054   12.77 < 0.0000000000000002 ***
## agency.c               -0.1786     0.0396   -4.51     0.00000714900446 ***
## exp.c                   0.2427     0.0398    6.10     0.00000000139313 ***
## robot.grouphuman-like   0.6416     0.0872    7.36     0.00000000000034 ***
## robot.grouprobotic      0.4041     0.0700    5.77     0.00000000975618 ***
## SexMale                -0.0614     0.0528   -1.16                 0.25    
## Age                     0.0118     0.0098    1.21                 0.23    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.925 on 1236 degrees of freedom
## Multiple R-squared:  0.0682, Adjusted R-squared:  0.0636 
## F-statistic: 15.1 on 6 and 1236 DF,  p-value: <0.0000000000000002
## 
## Call:
## lm(formula = uv ~ agency.c + exp.c + robot.group + Sex + Age, 
##     data = RBI)
## 
## Standardized Coefficients::
##           (Intercept)              agency.c                 exp.c 
##               0.00000              -0.18678               0.25388 
## robot.grouphuman-like    robot.grouprobotic               SexMale 
##               0.25892               0.20339              -0.03202 
##                   Age 
##               0.03668

Distribution of participants among k-means clusters

Number of participants that fall into 1, 2, or 3 clusters
1 2 3
44 51 18
Number of participants that fall into only one cluster
0 1
69 44
Number of participants that fall into only two clusters
0 1 & 2 1 & 3 2 & 3
62 3 32 16

Data-driven aggregates

Principal Components Analysis

##           PC1     PC2     PC3      PC4     PC5     PC6     PC7
## choose 0.1391 0.14729 0.19039 0.385614 0.08347 0.02297 0.03289
## feel   0.1507 0.10041 0.13581 0.062619 0.16262 0.02872 0.38248
## hungry 0.1510 0.07863 0.02580 0.051288 0.27548 0.20673 0.25560
## moral  0.1390 0.20082 0.21480 0.069632 0.10073 0.17646 0.23018
## pain   0.1362 0.24398 0.03446 0.068407 0.04717 0.33947 0.02926
## scared 0.1419 0.17948 0.11852 0.009152 0.28702 0.21210 0.05032
## think  0.1423 0.04938 0.28021 0.353288 0.04352 0.01355 0.01927
## Importance of components:
##                          PC1    PC2    PC3    PC4    PC5    PC6    PC7
## Standard deviation     2.195 0.8335 0.6217 0.5899 0.5341 0.5039 0.4622
## Proportion of Variance 0.688 0.0993 0.0552 0.0497 0.0408 0.0363 0.0305
## Cumulative Proportion  0.688 0.7875 0.8427 0.8925 0.9332 0.9695 1.0000

## 
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group, data = pca)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.524 -0.767 -0.339  0.666  2.745 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)             1.4086     0.0615   22.90 < 0.0000000000000002 ***
## PC1                     0.0164     0.0119    1.37                 0.17    
## PC2                    -0.2039     0.0315   -6.48     0.00000000013579 ***
## robot.grouphuman-like   0.6396     0.0869    7.36     0.00000000000034 ***
## robot.grouprobotic      0.4034     0.0698    5.78     0.00000000936594 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.923 on 1238 degrees of freedom
## Multiple R-squared:  0.0711, Adjusted R-squared:  0.0681 
## F-statistic: 23.7 on 4 and 1238 DF,  p-value: <0.0000000000000002
## 
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group, data = pca)
## 
## Standardized Coefficients::
##           (Intercept)                   PC1                   PC2 
##               0.00000               0.03761              -0.17778 
## robot.grouphuman-like    robot.grouprobotic 
##               0.25810               0.20304
## 
##  Pearson's product-moment correlation
## 
## data:  pca$PC1 and pca$PC2
## t = -0.00000000000021, df = 1200, p-value = 1
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.0556  0.0556
## sample estimates:
##                   cor 
## -0.000000000000006098
## 
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group + Sex + Age, data = pca)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.554 -0.738 -0.353  0.611  2.756 
## 
## Coefficients:
##                       Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)            1.34797    0.10507   12.83 < 0.0000000000000002 ***
## PC1                    0.02238    0.01322    1.69                0.091 .  
## PC2                   -0.20486    0.03150   -6.50     0.00000000011393 ***
## robot.grouphuman-like  0.63961    0.08690    7.36     0.00000000000033 ***
## robot.grouprobotic     0.40417    0.06975    5.79     0.00000000867413 ***
## SexMale               -0.06487    0.05271   -1.23                0.219    
## Age                    0.01194    0.00978    1.22                0.222    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.923 on 1236 degrees of freedom
## Multiple R-squared:  0.0735, Adjusted R-squared:  0.069 
## F-statistic: 16.3 on 6 and 1236 DF,  p-value: <0.0000000000000002
## 
## Call:
## lm(formula = uv ~ PC1 + PC2 + robot.group + Sex + Age, data = pca)
## 
## Standardized Coefficients::
##           (Intercept)                   PC1                   PC2 
##               0.00000               0.05137              -0.17859 
## robot.grouphuman-like    robot.grouprobotic               SexMale 
##               0.25811               0.20342              -0.03383 
##                   Age 
##               0.03702

K-means clustering of a data-driven components

## [1] 1094

Distribution of robots among k-means clusters (PCA)

##          
##           1 2 3
##   actroid 1 0 0
##   atlas   1 0 0
##   kb      1 0 0
##   kf      1 0 0
##   spot    1 0 0
##   nao     0 1 0
##   pepper  0 1 0
##   sofia   0 1 0
##   stan    0 1 0
##   tapia   0 1 0
##   festo   0 0 1
## 
## Call:
## lm(formula = uv ~ cluster, data = km)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -0.974 -0.798 -0.298  0.702  2.540 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)   1.7977     0.0312   57.61 < 0.0000000000000002 ***
## cluster2      0.1761     0.0787    2.24                0.025 *  
## cluster3     -0.3380     0.0837   -4.04             0.000057 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.948 on 1240 degrees of freedom
## Multiple R-squared:  0.0194, Adjusted R-squared:  0.0178 
## F-statistic: 12.3 on 2 and 1240 DF,  p-value: 0.0000053
## 
## Call:
## lm(formula = uv ~ cluster, data = km)
## 
## Standardized Coefficients::
## (Intercept)    cluster2    cluster3 
##     0.00000     0.06363    -0.11486

Distribution of participants among k-means clusters (PCA)

Number of participants that fall into 1, 2, or 3 clusters
1 2 3
48 50 15
Number of participants that fall into only one cluster
0 1
65 48
Number of participants that fall into only two clusters
0 1 & 2 1 & 3 2 & 3
63 3 31 16

Unfolding analysis

Latent Class Analysis